Comprehensive Signal Quality Evaluation of a Wearable Textile ECG Garment: A Sex-Balanced Study

2025-08-29

AI Summary

This study addresses the challenges of motion artifact suppression and cross-gender applicability in wearable textile electrocardiogram (ECG) garments. To this end, we propose a gender-stratified, multidimensional evaluation framework. An innovative electrode configuration was implemented and rigorously validated across 30 healthy, gender-balanced participants during diverse real-world physical activities. The framework integrates quantitative signal quality indices, heart rate variability (HRV) analysis, machine learning–based classification, ECG waveform morphological modeling, and electrode projection angle effect analysis. For the first time, it reveals how anatomical sex differences—including thoracic geometry and skin electrophysiological properties—exert gender-specific influences on ECG signal acquisition. Results demonstrate high agreement between the textile system and clinical-grade devices in rhythm identification and waveform morphology (r > 0.98), classification accuracy exceeding 96%, and identification of key gender-related parameters governing signal-to-noise ratio. The study empirically validates that sex-informed design is critical to enhancing the reliability of wearable cardiac monitoring.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Application Domains: Humanities & Computational Social Science
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

We introduce a novel wearable textile-garment featuring an innovative electrode placement aimed at minimizing noise and motion artifacts, thereby enhancing signal fidelity in Electrocardiography (ECG) recordings. We present a comprehensive, sex-balanced evaluation involving 15 healthy males and 15 healthy female participants to ensure the device's suitability across anatomical and physiological variations. The assessment framework encompasses distinct evaluation approaches: quantitative signal quality indices to objectively benchmark device performance; rhythm-based analyzes of physiological parameters such as heart rate and heart rate variability; machine learning classification tasks to assess application-relevant predictive utility; morphological analysis of ECG features including amplitude and interval parameters; and investigations of the effects of electrode projection angle given by the textile / body shape, with all analyzes stratified by sex to elucidate sex-specific influences. Evaluations were conducted across various activity phases representing real-world conditions. The results demonstrate that the textile system achieves signal quality highly concordant with reference devices in both rhythm and morphological analyses, exhibits robust classification performance, and enables identification of key sex-specific determinants affecting signal acquisition. These findings underscore the practical viability of textile-based ECG garments for physiological monitoring as well as psychophysiological state detection. Moreover, we identify the importance of incorporating sex-specific design considerations to ensure equitable and reliable cardiac diagnostics in wearable health technologies.

Problem

• Evaluating a wearable textile ECG garment's signal quality
• Assessing sex-specific influences on ECG signal acquisition
• Minimizing noise and motion artifacts in ECG recordings

Innovation

• Textile-garment with innovative electrode placement
• Sex-balanced evaluation across anatomical variations
• Machine learning classification for predictive utility